An NLP Lexicon as a Largely Language-Independent Resource

Abstract

This paper describes salient aspects of the OntoSem lexicon of English, a lexicon whose semantic descriptions can either be grounded in a language-independent ontology, rely on extra-ontological expressive means, or exploit a combination of the two. The variety of descriptive means, as well as the conceptual complexity of semantic description to begin with, necessitates that OntoSem lexicons be compiled primarily manually. However, once a semantic description is created for a lexeme in one language, it can be reused in others, often with little or no modification. Said differently, the challenge in building a semantic lexicon is describing semantics; once the semantics are described, it is relatively straightforward to connect given meanings to the appropriate head words in other languages. In this paper we provide a brief overview of the OntoSem lexicon and processing environment, orient our approach to lexical semantics among others in the field, and describe in more detail what we mean by the largely language-independent lexicon. Finally, we suggest reasons why our resources might be of interest to the larger community.

Subject ellipsis in Russian and Polish*

“…The real problem is that when syntax fails to predict the correct type or form of a fragment, Merchant falls back on an unspecified semantic recovery process. However, if the mechanisms for semantic recovery are necessary in the language system anyway , they must be available to all inputs, not only those structures for which no syntactic mechanisms are readily available (for further discussion, see McShane et al 2005b). Purely syntactic studies of ellipsis must be subject to similar scrutiny: will the generalizations put forth hold up when all of the phenomena sidelined as out of purview are reintroduced into a more comprehensive account?…”

Parameterizing mental model ascription across intelligent agents

“…Language understanding. Language understanding in OntoAgent involves preprocessing followed by morphological, syntactic, semantic and pragmatic analysis (see Nirenburg & Raskin 2004;McShane, Nirenburg & Beale 2005;McShane 2009;McShane & Nirenburg 2009; and references therein). The result of language analysis, when all goes well (semantically-oriented natural language understanding being, after all, a long-term challenge of artificial intelligence), is an unambiguous text meaning representation (TMR) written in the metalanguage of the OntoAgent ontology.…”

A Knowledge Representation Language for Natural Language Processing, Simulation and Reasoning

“…Instead, we describe the meaning of such words compositionally in the lexicons of those languages that do use it. (For further discussion of the lexicon/ ontology split, see [13]. )…”

“…And third, the sem-strucs (and often the associated syn-strucs as well) from the lexicon for one language can very often be ported into the lexicon of another language with little or no modi¯cations, which greatly enhances the multi-lingual applicability of the OntoAgent suite of resources. For discussion of the cross-lingual use of OntoAgent lexicons, see [13]. Whereas the ontology contains ontological concepts, like CITY and WAR, the fact repository contains remembered instances of those concepts, like London (say, and World War II (say, WAR-4).…”